Swarm

Swarm

Before delegating, read the harness instructions and the active harness file. Map agent names, tool arguments, and model roles there.

Fan out N parallel cloud workers. They may cover separate slices, race the same brief, or mix both. The parent waits, aggregates, and returns one report.

Start

Open a todolist with one entry per phase before launching anything.

1. Frame
2. Fan out
3. Aggregate
4. Report

Phase A: Frame

1. State the done predicate and the artifact or report the swarm must return.
2. Choose the shape. Partition into slices, race N workers on identical briefs, or mix both. For a race or mixed shape, declare first pass, rank all, or best-of before spawning.
3. Set N from the user or derive it from the shape. N is total workers, not the cloud concurrency limit.
4. Pick the worker model from swarm workers in ~/.pstack/models.md when present. Otherwise use the active harness file's swarm workers default. For a model race, name each arm's model up front, and skip the race entirely on a harness with no per-subagent model override.
5. Give each worker its own writable output when it writes.

Phase B: Fan out

Spawn all N workers in one message, backgrounded, with the configured model, using the subagent tool the active harness file names. Cursor takes environment: "cloud" for workers that need no local access; every other harness runs workers locally, so give each one its own worktree or output directory instead (Phase A step 5) and treat the harness file's "long-running work" section as the ceiling on how detached a worker can be.

When a worker must start from a non-default pushed branch, pass cloud_base_branch.

Every brief stands alone. Include the goal, scope, exact slice or race arm, how to verify, and what to report. Reports use PASS, ISSUES, or BLOCKED with evidence.

If a worker drops out, proceed with N-1 and note it.

Phase C: Aggregate

Read the terminal results. For coverage, every required slice needs a result. For a race, apply the selection rule declared up front. Use first pass, rank all, or best-of. Do not paste raw worker dumps.

Keep a compact result table, one-line evidenced issues, and explicit gaps or dropouts.

Phase D: Report

Return one consolidated in-chat report with the table, issue one-liners, gaps or dropouts, and the race rule when used.